Security-Constrained Optimal Power Flow Solved With a Hybrid Multiswarm Particle Swarm Optimizer

• Published in: IEEE Power & Energy Society General Meeting pp. 1 - 5
• Main Authors: Zhang, Haixiang, Xiao, Dongliang, Liu, Jianan, Qiao, Wei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2019
• Subjects: Comprehensive learning (CL); Convergence; Costs; Cultural differences; Diversity reception; Hybrid power systems; Load flow; Particle swarm optimization; particle swarm optimization (PSO); Security; security-constrained optimal power flow (SCOPF); Simulated annealing; simulated annealing (SA); Simulation
• ISSN: 1944-9933
• DOI: 10.1109/PESGM40551.2019.8974078

This paper presents a hybrid multiswarm particle swarm optimization (HMPSO) algorithm to solve the security-constrained optimal power flow (SCOPF) problem, which aims to minimize the predefined cost while taking both system capacity requirement and operating security constraints into account. The HMPSO is based on the comprehensive learning (CL) strategy and is hybridized with the simulated annealing (SA) algorithm. The swarm population is divided into multiple subpopulations. One of the subpopulations is assigned for exploitation and the rest subpopulations are responsible for exploration. The CL strategy is used to create the exemplars for all subpopulations. By combining with the SA, the HMPSO builds an asymmetrical bidirectional transmission channel to exchange information between the explorative subpopulations and the exploitative subpopulation, which can enhance both the exploration and exploitation. Numerical results demonstrate the superior performance of the proposed HMPSO over three reference PSO algorithms in the literature.